Evaluasi Kinerja Random Forest, SVM, dan Transformer untuk Klasifikasi Komentar Judi Online di Youtube

Authors

  • Devina Arroyan UIN Walisongo Semarang
  • Maya Rini Handayani UIN Walisongo Semarang
  • Khothibul Umam UIN Walisongo Semarang
  • Masy Ari Ulinuha UIN Walisongo Semarang

DOI:

https://doi.org/10.26418/justin.v14i1.94059

Keywords:

komentar YouTube, judi online, klasifikasi teks, Random Forest, SVM, IndoBERT

Abstract

Maraknya komentar bermuatan promosi judi online di platform YouTube menimbulkan kekhawatiran terhadap kenyamanan dan keamanan digital, khususnya bagi pengguna muda. Penelitian ini bertujuan mengevaluasi kinerja tiga metode klasifikasi teks dalam mendeteksi komentar judi online berbahasa Indonesia, yaitu Transformer (IndoBERT), Support Vector Machine (SVM), dan Random Forest. Dataset yang digunakan terdiri dari 5.000 komentar hasil ekstraksi dari beberapa video YouTube yang kemudian melalui proses pelabelan manual dan prapemrosesan teks. Proses evaluasi dilakukan menggunakan skema pembagian data latih–uji sebesar 80:20 dengan metrik akurasi, precision, recall, dan F1-score sebagai ukuran performa. Hasil menunjukkan bahwa IndoBERT memberikan performa terbaik dengan akurasi 98,70% dan F1-score 0,98, lebih tinggi dibandingkan SVM (88,85%) dan Random Forest (79,62%). Studi ini memiliki keterbatasan pada jumlah dan keragaman dataset yang masih terbatas, sehingga performa model berpotensi berubah ketika diterapkan pada skala data yang lebih luas atau domain komentar lain. Penelitian lanjutan dapat mempertimbangkan penambahan data dari berbagai kategori konten YouTube serta penerapan teknik augmentasi data untuk meningkatkan generalisasi model.

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Published

2026-01-30

Issue

Vol. 14 No. 1 (2026)

Section

Articles

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